Model interpretability is currently used to bridge the lack of trust in models in machine learning. It can be approached by model: intrinsic or post-hoc, by scope: local or global, or by method: model-specific or model-agnostic. They have the common goal to present concise and intelligible information to a human user that empowers them to determine whether or not the system is behaving in a safe manner. LIME and SHAP are among the most popular explainable methods. Both are post-hoc, local and model-agnostic. Nevertheless they come with a variety of drawbacks. Some of the disadvantages of these methods are that they require a human to perform a verification task to certify if the model behaves as expected, nonetheless, the process is error prone. Moreover, post-hoc explanation techniques might be unstable and unreliable [1]}. Ghorbani et al. show that some explanation techniques can be highly sensitive to small perturbations in the input even though the underlying classifier's predictions remain unchanged [2]}.
